Popular GitHub Repositories for Hyperopt

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.

Using Kafka-Python to illustrate a ML production pipeline

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

Allstate Kaggle Competition ML Capstone Project

? Distributed machine learning made simple.

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning :rocket:

Machine Learning Tool Box

Different hyperparameter optimization methods to get best performance for your Machine Learning Models

Fair quantitative comparison of NLP embeddings from GloVe to RoBERTa with Sequential Bayesian Optimization fine-tuning using Flair and SentEval. Extension of HyperOpt library to log_b priors.

E2E MLOps with Databricks

Machine learning's parameter search and feature selection module which is integrated log management and visualization.

Kakapo (KAH-k?-poh) implements a standard set of APIs for outlier detection at scale on Databricks. It provides an integration of the vast PyOD library of outlier detection algorithms with MLFlow for tracking and packaging of models and hyperopt for exploring vast, complex and heterogeneous search spaces.

Hyperparameter optimisation utility for lightgbm and xgboost using hyperopt.

A solution to the Higgs boson machine learning challenge

We use multiple Tree boosting models and compare their performance to calculate the fit percentage of a candidate for the job they apply for. Also try to handle categorical methods using various methods.

Topic modelling - optimization of model hyper params. Notebooks are examples of using our optimization pipeline for sample data.

MLOPS applied for rasa

The bayesian_optimization service has been developed to allow clients to make hyperparameter fine-tuning. The bayesian_optimization service is a wrapper of the Hyperopt python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.